class_docstr: An one-hot encoding pre-processor for categorical features.
      
docstr: implementation of
  :class:`~maze.core.wrappers.observation_preprocessing.preprocessors.base.PreProcessor` interface
docstr_tok: ["implementation","of",":","class",":","`","~maze.core.wrappers.observation_preprocessing.preprocessors.base.PreProcessor","`","interface"]
code: def processed_shape(self) -> Tuple[int, ...]:
      """implementation of
      :class:`~maze.core.wrappers.observation_preprocessing.preprocessors.base.PreProcessor` interface
      """
      if isinstance(self._original_observation_space, spaces.Box):
          high = int(np.max(self._original_observation_space.high) + 1)
          return tuple(
              list(self._original_observation_space.shape) + [high]
          )
      elif isinstance(
          self._original_observation_space, spaces.Discrete
      ):
          return (self._original_observation_space.n,)
      else:
          raise ValueError(
              f"{type(self._original_observation_space)} not supported!"
          )
  
file_path: maze/maze/core/wrappers/observation_preprocessing/preprocessors/one_hot.py
imports: from typing import Tuple
  import numpy
  from gym import spaces
  from maze.core.wrappers.observation_preprocessing.preprocessors.base import PreProcessor
  from maze.core.annotations import override
label_desc: Use image node_id 1 for calling the OneHotPreProcessor obj's underlying member method code with example usage: obj.processed_shape() and returns: tuple, self